The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

Taha Mohseni Ahooyi; Benjamin J. Stear; J. Alan Simmons; Vincent T. Metzger; Praveen Kumar; John Erol Evangelista; Daniel J. B. Clarke; Zhuorui Xie; Hee-Su Kim; Sherry L. Jenkins; Mano Ram Maurya; Srinivasan Ramachandran; Eoin Fahy; Thomas H. Gillespie; Fahim T. Imam; Natallia Kokash; Matthew E. Roth; Robert L. Fullem; Dubravka Jevtic; Aleks Mihajlovic; Michael Tiemeyer; Clara Bakker; Andrew J. Schroeder; Julia Markowski; Jared L. Nedzel; Dave D. Hill; James Terry; Chris Nemarich; Jyl Boline; Peter J. Park

doi:10.1101/2025.08.11.666099

BioinformaticsbioRxivAug 11, 2025

Preprint

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

Taha Mohseni Ahooyi iD, Benjamin J. Stear iD, J. Alan Simmons, Vincent T. Metzger iD, Praveen Kumar iD, John Erol Evangelista iD, Daniel J. B. Clarke iD, Zhuorui Xie iD, Hee-Su Kim iD, Sherry L. Jenkins iD, Mano Ram Maurya iD, Srinivasan Ramachandran iD, Eoin Fahy iD, Thomas H. Gillespie, Fahim T. Imam iD, Natallia Kokash iD, Matthew E. Roth iD, Robert L. Fullem iD, Dubravka Jevtic, Aleks Mihajlovic, Michael Tiemeyer iD, Clara Bakker iD, Andrew J. Schroeder iD, Julia Markowski iD, Jared L. Nedzel iD, , Deanne M. Taylor iD

PDF DOI

Post to X

Threads on X Post to Bluesky Threads on Bluesky

Abstract

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.